The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility. The authors thank Tom Mroz, Melinda Morrill, John Cawley, Michael Pesko, and seminar participants at the Federal Reserve Bank of Atlanta and the Southeastern Micro Labor Workshop for helpful comments. They also thank Patrick Henson for excellent research assistance. Please address questions regarding content to Michael Darden, Carey Business School, Johns Hopkins University, 100 International Drive, Baltimore, MD 21202, michaeldarden@jhu.edu; Julie Hotchkiss, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, julie.l.hotchkiss@atl.frb.org; or Melinda Pitts, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, mmpitts94@gmail.com. Federal Reserve Bank of Atlanta working papers, including revised versions, are available on the Atlanta Fed’s website at www.frbatlanta.org. Click “Publications” and then “Working Papers.” To receive e-mail notifications about new papers, use frbatlanta.org/forms/subscribe.

FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES

The Dynamics of the Smoking Wage Penalty Michael E. Darden, Julie L. Hotchkiss, and M. Melinda Pitts Working Paper 2020-11 July 2020 Abstract: Cigarette smokers earn significantly less than nonsmokers, but the magnitude of the smoking wage gap and the pathways by which it originates are unclear. Proposed mechanisms often focus on spot differences in employee productivity or employer preferences, neglecting the dynamic nature of human capital development and addiction. In this paper, we formulate a dynamic model of young workers as they transition from schooling to the labor market, a period in which the lifetime trajectory of wages is being developed. We estimate the model with data from the National Longitudinal Survey of Youth, 1997 Cohort, and we simulate the model under counterfactual scenarios that isolate the contemporaneous effects of smoking from dynamic differences in human capital accumulation and occupational selection. Results from our preferred model, which accounts for unobserved heterogeneity in the joint determination of smoking, human capital, labor supply, and wages, suggest that continued heavy smoking in young adulthood results in a wage penalty at age 30 of 14.8 percent and 9.3 percent for women and men, respectively. These differences are less than half of the raw mean difference in wages at age 30. We show that the contemporaneous effect of heavy smoking net of any life-cycle effects explains roughly 67 percent of the female smoking wage gap but only 11 percent of the male smoking wage gap. JEL classification: I10, I12 Key words: wages, smoking; dynamic system of equations https://doi.org/10.29338/wp2020-11

The Dynamics of the Smoking Wage Penalty∗

Michael E. DardenJohns Hopkins University and NBER

Julie L. HotchkissFederal Reserve Bank of Atlanta and Georgia State University

M. Melinda PittsFederal Reserve Bank of Atlanta

July, 2020

Abstract

Cigarette smokers earn significantly less than nonsmokers, but the magnitude of the smoking wage gap and the pathways by which it originates are unclear. Proposed mechanisms often focus on spot differences in employee productivity or employer preferences, neglecting the dynamic nature of human capital development and addiction. In this paper, we formulate a dynamic model of young workers as they transition from schooling to the labor market, a period in which the lifetime trajectory of wages is being developed. We estimate the model with data from the National Longitudinal Survey of Youth, 1997 Cohort, and we simulate the model under counterfactual scenarios that isolate the contempora-neous effects of smoking from dynamic differences in human capital accumulation and occupational selection. Results from our preferred model, which accounts for unobserved heterogeneity in the joint determination of smoking, human capital, labor supply, and wages, suggest that continued heavy smok-ing in young adulthood results in a wage penalty at age 30 of 14.8% and 9.3% for women and men, respectively. These differences are less than half of the raw mean difference in wages at age 30. We show that the contemporaneous effect of heavy smoking net of any life-cycle effects explains roughly 67% of the female smoking wage gap but only 11% of the male smoking wage gap.

JEL Classification: I10; I12;

Keywords: Wages, Smoking; Dynamic System of Equations

∗We thank Tom Mroz, Melinda Morrill, John Cawley, Michael Pesko, and seminar participants at the Federal ReserveBank of Atlanta and the Southeastern Micro Labor Workshop for helpful comments. We also thank Patrick Henson forexcellent research assistance. The views expressed in this work are those of the authors and do not reflect the opinion of theFederal Reserve Bank of Atlanta or the Federal Reserve System.

1 INTRODUCTION

1 Introduction

Smoking cigarettes is associated with well-documented expected longevity (Doll et al., 2004; Darden et al.,

2018) and health care expenditure (Sloan et al., 2006; Xu et al., 2015) effects. In addition, smokers earn

between 2% and 24% lower wages than nonsmokers (Auld, 2005; Grafova & Stafford, 2009; van Ours,

2004), but neither the magnitude nor the mechanisms behind the smoking wage gap are well understood.

A simple set of explanations is that smokers are less productive due to higher rates of absenteeism and

illness, and they generate higher health care costs, which may be particularly important to firms that

self-insure.1 Similarly, employers may have preferences (increasingly over time) for nonsmokers, and the

smoking wage gap may represent a form of discrimination in which labor market opportunities are less

forthcoming to smokers (Hotchkiss & Pitts, July 2013). While the literature has focused on these types of

static mechanisms, differences in wages are inherently a dynamic process: smoking behavior and human

capital formation begin at young ages, and these endogenous states influence decisions regarding education,

labor supply, smoking, and occupation. Failing to account for the joint determination of these histories will

lead to biased estimates of the smoking wage gap. Furthermore, there is little empirical evidence on the

importance of these life-cycle mechanisms relative to static explanations, especially in early-career workers

for whom human capital is still developing.

We estimate a dynamic model of human capital accumulation, wage determination, and smoking be-

havior to decompose static and life-cycle mechanisms behind the smoking wage gap. In our model, state

variables that capture past smoking behavior, work decisions, and human capital accumulation influence

the joint decisions to work, smoke, and seek education. Conditional on working, we observe wages and

occupational task requirements, which we define as factors that characterize occupations on the basis of

mental reasoning and physical strenuousness.2 While task requirements shed light on productivity differ-

ences as a mechanism for the smoking wage gap, explicitly modeling the sequence of school enrollment

decisions characterizes the extent to which those who choose to smoke accumulate lower human capital. An

important feature of our dynamic empirical model is the treatment of unobserved heterogeneity, which we

1Berman et al. (2014) estimate the excess health care cost to be $5,816 per worker.2These continuous occupational factors stem from principal component analysis and are highly correlated with the discrete

occupational classifications used in the literature (Autor, 2019).

1

1 INTRODUCTION

allow to be flexibly correlated across behavioral and outcome equations. If, for example, unobserved factors

cause some individuals to be more likely to select into physically demanding jobs and to be more likely to

start (and continue) smoking, we allow for this correlation. Finally, given that there are approximately 55

million former smokers in the United States, our model allows us to investigate the extent to which wages

of former smokers converge to those of nonsmokers (Creamer et al., 2018).

We estimate our model with geocoded data from the National Longitudinal Survey of Youth, 1997

Cohort, which allows us to (a) evaluate competing mechanisms for the wage gap among a modern cohort of

workers and (b) track the initial wage trajectories for smokers and nonsmokers, beginning with schooling

and proceeding through a person’s early career. In our raw data, the smoking wage gap between nonsmokers

and near-daily smokers emerges by age 23 and grows to roughly 40% for women and 25% for men by age

30. To demonstrate the importance of accounting for observed and unobserved heterogeneity, we simulate

our estimated model in which we impose different patterns of smoking behavior from age 16 through

age 30 while updating state variables that capture past smoking, work, and education decisions. These

simulations suggest that the selectivity-corrected age 30 smoking wage gap is 14.8% and 9.3% for women

and men, respectively. The dramatic reduction between our simulated smoking results and those from the

raw data is due to the strong positive correlation in factors that drive abstinence from smoking and human

capital accumulation. These findings are broadly consistent with the literature (Auld, 2005; Grafova &

Stafford, 2009; van Ours, 2004), but they stem from a model-based approach which is conducive to isolating

mechanisms.

Our simulated results reflect the total marginal effect of smoking on wages – the combination of static

and life-style mechanisms. To separate the static mechanisms of smoking on wages, we simulate our model,

updating schooling, employment, and occupation decisions/outcomes as though an individual does not

smoke while imposing different smoking behaviors in the determination of wages. Thus, wages progress

as a function of nonsmoker employment, education, and occupation paths, but, in some simulations, as

though individuals were smoking when wages are determined. We call this the contemporaneous effect of

smoking on wages, and our simulation results imply that this contemporaneous effect explains 67% the total

female smoking wage gap but only 11% of the male gap. That is, the majority of the smoking wage gap

2

1 INTRODUCTION

for men stems from factors that are pre-determined when wages are set; for women, on the other hand, the

contemporaneous effect explains the majority of the smoking wage gap. This finding - that mechanisms

behind the smoking wage gap are very different for men versus women - extends the literature that the

smoking wage gap differs by gender (van Ours, 2004).

Part of the difference in contemporaneous versus life-cycle effects by gender is due to occupation. For

women at age 30, the smoking wage gap is increasing in the amount of mental reasoning required in an

occupation - the smoking wage penalty is approximately 40% larger for women in the most mentally taxing

occupations relative to the least - but invariant to its physical strenuousness. For men, the smoking wage

gap is nearly 100% larger in the most mentally taxing jobs; however, the gap is becomes smaller in the degree

of physical strenuousness. The smoking wage gap is roughly 36% smaller for men in the most physically

demanding occupations relative to the least. This creates an incentive for male smokers to select into

more physically strenuous occupations, where the penalty for smoking is lower. As educational attainment

translates to more mentally rigorous and less physically strenuous occupations, the opportunity cost of an

additional year of schooling for male smokers is higher than for female smokers. Our findings with respect to

physical strenuousness also suggest that productivity and health differences, which should be exacerbated

as an occupation becomes more physically demanding, are unlikely to drive the smoking wage gap for either

men or women.

Both the magnitude and the mechanisms behind the smoking wage gap have significant policy relevance.

Not only does smoking have implications for individual lifetime earnings, but the external implications for

lower income tax revenues and productivity could be significant. Food and Drug Administration (FDA)

cost-benefit and cost-effectiveness analyses already take these consequences into account (e.g., MacMonegle

et al. (2018)), and our results may inform future regulatory action, including regulatory action with respect

to substitute nicotine delivery products such as e-cigarettes (Cotti et al., 2020). Accounting for selection

into smoking and individual heterogeneity, our analysis provides a more accurate estimate of the direct

impact of smoking on wages, and we find that while the smoking wage gap is larger for women than for men,

the returns to quitting are also more immediate and significant for women.

3

2 BACKGROUND

2 Background

The debate over regulating tobacco use in the U.S. dates back at least to 1964 when then U.S. Surgeon

General at the time, Dr. Luther Terry, released the first government report concluding that cigarette

smoking causes lung cancer, in addition to other types of cancer. The report also concludes, while there

is no one characteristic that is uniquely identifiable with smokers, “The overwhelming evidence points to

the conclusion that smoking—it’s beginning, habituation, and occasional discontinuation—is to a large

extent psychologically and socially determined.” The report also makes a point to say that this conclusion

“does not rule out. . . the existence of predisposing constitutional or hereditary factors.”3 The question,

then, for public health officials, was whether smoking is a behavior that can be modified through social and

psychological intervention, or whether it’s just that smokers will be smokers and efforts to modify behavior

will be ineffectual.

There is significant evidence that public health campaigns, smoking restrictions, and taxation can affect

smoking behavior, indicating that the decision to smoke has more to do with psychological and social

influences than hereditary factors. For example, Bauer et al. (2005) found that individuals in smoke-free

work environments were 1.9 time more likely to stop smoking than those whose work places were not

restricted (also see Fichtenberg & Glantz (2002)). Additionally, youth living in towns with strict restaurant

smoking regulations were significantly less likely to progress from experimental to established smoking

behavior than youth in locations with weak restaurant smoking regulations Siegel et al. (2009).

Regarding public health campaigns, ”The Real Cost!” anti-smoking campaign was estimated to have

prevented about 350,000 youth from starting smoking between 2014 and 2016 (MacMonegle et al. (2018)).

However, of course, some campaigns are found to be more effective than others (for example, see Davis

& Carpenter (2009)). The combined effectiveness of anti-smoking campaigns and regulations during the

past half-century can be seen in the significant decline in the number of per capita smokers in the U.S.

After rising steadily during the first half of the twentieth century, the rise in smokers per capita plateaued

in the 1950s-1970s, then embarked on a dramatic decline through today (Centers for Disease Control and

3See page 40. HHS (1964)

4

2 BACKGROUND

Prevention, 2019; National Center for Chronic Disease Prevention and Health Promotion (US) Office on

Smoking and, 2014).

Another effective deterrent to smoking, to varying degrees, has been found to be the imposition of excise

taxes on cigarettes. While the general consensus is that imposing or raising taxes will reduce smoking

(Chaloupka et al., 2012), a number of studies have found that a very large increase in taxes is required to

reduce smoking by any meaningful amount (Callison & Kaestner, 2014). And while most evidence points

to youth smoking behavior being more sensitive to taxes (i.e., price) (for example, see Carpenter & Cook

(2008)), Hansen et al. (2017) found that the impact on taxes on youth smoking has all but disappeared

more recently.

The health benefits of reduced smoking are clear. The CDC estimates that smoking related illnesses

in the United States cost nearly $300 billion each year (U.S. Department of Health and Human Services,

2014), and in 2010, nearly nine percent of healthcare expenditures in the U.S. were attributed to smoking

cigarettes (Xu et al. (2015)). However, efforts to restrict, cajole, or incentivize people to not start or to

quit smoking come at a cost, in terms of resources expended for enforcement, advertisement, and lost

consumer surplus (Lemieux (2019)), not to mention the cost to the tobacco companies and tobacco farmers

from reduced demand. By Presidential executive order, all government agencies are required, “in choosing

among alternative regulatory approaches, those approaches that maximize net benefits (including potential

economic, environmental, public health and safety, and other advantages; distributive impacts; and equity)”

(White House, 2011, p. 3821). Policy makers have so far concluded that the benefits in terms of fewer deaths,

fewer chronic smoking-related illnesses, and lower overall healthcare expenditures are worth the costs of

regulating tobacco consumption. And, indeed, in the case of taxation, there is a bonus of increased revenue

(Congressional Budget Office, 2012). Finally, it’s clear that consideration of costs versus benefits of further

regulation are explicit in FDA deliberations: “FDA also recognizes that, if FDA were to proceed to the

stage of proposing a rule in this area, potential costs and benefits from a possible nicotine tobacco product

standard would be estimated and considered in an accompanying preliminary impact analysis, including

the potential impacts on growers of tobacco and current users of potentially regulated products.” (Tobacco

Product Standard for Nicotine Level of Combusted Cigarettes (Proposed Rules), 2018, p. 11820).

5

3 EVIDENCE FROM NLSY

While pro-tobacco and libertarian influences have successfully convinced FDA to weigh the loss of con-

sumer surplus associated with restricting access or making smoking costlier against the health benefits of

further regulation, the economic benefit through higher wages, higher income tax revenue, and, potentially,

higher productivity should also explicitly be used as motivation for regulation that reduces smoking initi-

ation or induces quitting. This paper considers that direct benefit by providing a model-based estimate of

the wage penalty smokers experience during crucial early years of human capital accumulation and work ex-

perience. Furthermore, we show how smoking cessation affects wages; we decompose our results by gender;

and we investigate mechanisms that drive wage differences.

3 Evidence from NLSY

The National Longitudinal Survey of Youth, 1997 Cohort (NLSY97) offers a unique look at human capital

accumulation and early-career wage progression among a nationally representative sample of the United

States. The data contain rich, longitudinal information on wages, occupation, employment, education, and

smoking for up to 18 years, beginning when respondents were between the ages of 12 and 18. Our data

include geocoded information that, in some cases, is down to the Metropolitan Statistical Area (MSA)

level, which allows us to characterize local labor market conditions and local tobacco control policies. In

addition to local area unemployment rates that are included with NLSY data, we merge information on

local economic conditions at the county level from Bartik et al. (2017). These data include shares of

manufacturing and service occupations, as well as detailed labor market characteristics. Furthermore, we

merge local area tobacco control measures, including state, county, and in some cases, MSA indoor smoking

bans in restaurants, bars, and workplaces, and cigarette price levels and excise taxes from Americans for

Nonsmokers’ Rights.4 Finally, because early career occupational selection may help to explain the smoking

wage gap, we merge occupational task requirements from O*NET at the 3-digit occupational code level.

Thus, our data neatly characterize the environment in which teenagers make initial decisions regarding

smoking and human capital formation and in which young adults realize early career wage outcomes over

up to 18 years. While the NLSY 1979 cohort offers a longer panel, smoking information is only sparsely

4Data are available at https://no-smoke.org/materials-services/lists-maps/

6

3 EVIDENCE FROM NLSY

available, and the 1997 cohort offers a much more modern perspective on the smoking wage gap.

We restrict our attention to those individuals observed between 1997 and 2015 with no missing infor-

mation on location, education, labor supply, and daily smoking.5 Importantly, we do not drop individuals

observed to attrit from the sample nor do we drop individuals who miss one or more NLSY waves; instead,

we jointly model attrition with other endogenous variables, and we allow the error structure in the attrition

equation to be correlated with all other equations. Table 1 provides precise details about how we construct

our sample. The unit of time in our data is age, and we structure the data such that the first of up to 17

waves of information is at age 16.6 To study wage differences between smokers, former smokers, and never

smokers, we construct a panel of 5,529 individuals who generate 93,993 person/year observations and who

facilitate the estimation of the dynamic model presented in Section 4. Because of the restrictions necessary

to generate our estimation sample, we do not employ survey weights provided by NLSY. Instead, in all

models in which we condition on covariates, we also condition on the survey weight. We split all of our

empirical work by gender such that we have separate samples of 2,675 women and 2,854 men.

Table 1 Goes Here

Figure 1 demonstrates the smoking wage gap. We define smoking based on the number of days in which a

person reports smoking over the month preceding the NLSY interview. We define light and heavy smoking

as smoking on fewer than 20 of the past 30 days and smoking on 20 or more of the last 30 days, respectively.

By focusing on number of days smoked, rather than defining smoking based on the number of cigarettes per

day, we are able to access two additional waves of the NLSY 1997 (2013 and 2015); our results are robust

to alternative smoking definitions and classifications.7 Log wages are defined as the natural logarithm of

reported hourly cents per hour from an individual’s main job. All dollar figures are converted to real, 2015

dollars using the Consumer Price Index. For both men and women, Figure 1 demonstrates a significant

wage gap that emerges by age 23 and grows over time. By age 30, the gap between heavy smokers and

5We also impose that an individual must have been in school in 1997 as every state had compulsory education attendancethrough at least 16 years of age in 1997. https://nces.ed.gov/programs/digest/d97/d97t152.asp

6Because of the initial variation in age in 1997, we do not observe all individuals at all ages. Thus, we focus our descriptiveand econometric results on ages 16 though 30.

7These results are available by request.

7

3 EVIDENCE FROM NLSY

nonsmokers is 40% (25%) for women (men). Interestingly, the smoking wage gap for both genders is mainly

a difference between heavy smokers (i.e., near-daily smokers) relative to nonsmokers. Indeed, for both men

and women, intermittent smoking is not associated with a significant wage gap at age 30.

Figure 1 Goes Here

To investigate the trends in Figure 1, Table 2 presents summary statistics of all exogenous and pre-

determined variables in our dynamic model by smoking status and gender. Over our sample, 17.6% of

women and 22.5% of men report heavy smoking. Among the exogenous variables, there is a notable gap

in Armed Services Vocational Aptitude Battery (ASVAB) scores between heavy and nonsmokers for men

(37.3 vs. 41.1), but no such difference for women. For both men and women, Blacks and Hispanics are less

likely to report heavy smoking. Table 2 demonstrates the richness of exogenous characteristics in our data.

Employment share data are at the county level, as are the local area cigarette taxes. Furthermore, our data

include a rich set of county and state level indoor smoking ban information for workplaces. We define these

based on their geographic coverage (i.e., partial or complete within geographic area) and whether they are

qualified or complete. Pre-determined variables are those variables determined by our model but which

represent a fixed state when decisions are being made. These variables highlight the importance of selection.

For example, heavy smokers have, on average more than one fewer years of schooling than never smokers

and are much less likely to be college graduates relative to nonsmokers. Furthermore, heavy smokers have

lower mean tenure (working within the same occupation) than never smokers.

Table 2 Goes Here

Because early career occupational selection may be an important driver of wage differences between smokers

and nonsmokers, we merge O*Net data at the 2010 3-digit occupation code. These data include a plethora

of measurements that characterize an occupation by the tasks required on a daily basis. We follow the

literature and aggregate these measurements using Principal Component Analysis (PCA). Specifically, we

construct two latent factors, and Table 3 shows the measurements that enter our PCA for each factor, as well

8

3 EVIDENCE FROM NLSY

as the weight associated with the first principal component for each factor. The first factor, which captures

roughly 53% of the variation in measurements, loads strongly on communication skills, comprehension, and

deductive and inductive reasoning. We name this factor “mental reasoning.” The second factor, which

explains roughly 63% of the variation in measurements, loads heavily on measurements that capture the

physical exposure that workers place on their bodies. We name this factor “physical strenuousness.” Each

factor is normalized to be between 0 and 1, where 1 represents the most task complexity. Factors for mental

reasoning and physical strenuousness are highly correlated with factors from Autor (2019) for non-routine

interactive and routine manual occupations, respectively. We focus on latent factors generated by PCA

because they provide continuous metrics for task intensity, which we use to investigate heterogeneity in the

smoking wage gap.8

Table 3 Goes Here

Table 4, which presents the means for the model’s endogenous variables by smoking status, demonstrates

potential sources of the smoking wage gap. Specifically, heavy smokers are significantly less likely to be

enrolled in school and more likely to be working full-time. This point highlights the dynamics of the smoking

wage gap - smokers fail to invest in human capital at young ages; their initial wages are higher because they

are working full-time, but over time never smokers earn more. Conditional on working, heavy smokers work

in occupations that require lower mental reasoning, a factor that is strongly associated with wages, and

higher physical strenuousness. When averaged over the entire sample period, female heavy smokers earn

approximately 21% lower wages than nonsmokers, and that gap for men is roughly 6%.

Table 4 Goes Here

To demonstrate the dynamic importance of the endogenous variables in Table 4, Figure 2 presents the age

profile of school enrollment and occupation variables by smoking status separately for women and men.

8We have estimated our model with a binary equation for nonroutine interactive occupations instead the two continuouslatent factors, and our results are very similar. These results are available upon request.

9

4 DYNAMIC MODEL

Recall that our data are constructed such that all sample individuals are enrolled in school at age 16. By

age 17, for both genders, heavy smokers drop school enrollment much faster than nonsmokers. By age 19,

the proportion of heavy smokers enrolled in school is roughly 27 percentage points lower than non-smokers

for both men and women. The figure also shows how mental reasoning and physical strenuousness of one’s

occupation evolve by smoking status. By age 23, a significant gap in the mental reasoning required in an

occupation emerges between heavy smokers and nonsmokers. For both men and women, heavy smokers

work in more physically strenuous occupations, a difference that is relatively constant over the ages we

study. Figure 2 demonstrates the dynamic nature of the smoking wage gap. Smokers leave school earlier,

accumulating less human capital; they enter the workforce earlier and work in occupations that are more

physically strenuous and less mentally rigorous, all of which are associated with lower wages.

Given the descriptive evidence in Tables 2 and 4 and Figure 2,the wage trends in Figure 1 are difficult

to interpret for several reasons. First, smoking behavior is not time invariant. Individuals start and stop

smoking at different ages, and it remains an open question if those who quit smoking see convergence in

wages. Second, the sample changes over the age profile through selective attrition. To maximize sample

size, we include those leaving the sample prior to age 30, so the fact that the wage gap at age 30 is larger

than at age 25 is a function of both the underlying wage dynamics and changes in sample composition due to

attrition. Finally, wages at age 30 are a function of complex interactions between past smoking, education,

and employment behaviors and occupational outcomes, as illustrated in Figure 2. The remainder of this

paper develops a dynamic model of these outcomes and behaviors that helps to explain the mechanisms

that drive the wage trends in Figure 1.

4 Dynamic Model

Recognizing the limitations of the statistics above, we propose a dynamic system of equations estimator

that approximates a more general dynamic stochastic structural model.9 The empirical model accounts

for selection into both smoking and employment by jointly estimating behavioral equations for enrollment

in school, employment, and smoking, as well as outcomes for the mental and physical strenuousness of

9Examples of similar estimators include Gilleskie et al. (2017) and Darden et al. (2018).

10

4.1 Structural Equations 4 DYNAMIC MODEL

occupations and wages. To account for dynamic selection caused by non-random sample attrition, the

model also captures the endogenous probability of leaving the sample. Finally, we allow initial conditions

(age 16) for smoking and employment to shift the distribution of permanent unobserved heterogeneity

that flexibly affects each behavior and outcome equation. In what follows, we outline (rather than solve)

the dynamic structural model; we explain how the structural model informs the system of demand and

outcome equations which we jointly estimate; and we discuss the exclusion restrictions and functional form

assumptions which guarantee identification of the system.

4.1 Structural Equations

The structural model follows from standard labor supply theory in which an individual chooses whether

and how much to work based on a wage/job task draw. Because our goal is to decompose the mechanisms

that lead smokers to realize lower wages, we augment this model to allow for endogenous smoking and

school enrollment decisions. Consistent with the data in Section 3, we start the model at age 16, when all

individuals are assumed to be enrolled in school. The timeline picture below documents a representative age

t. Individual i enters representative age t with vector of state variables Θt, which include past work history

(both labor supply and occupation choices), human capital accumulation, and past smoking behavior.10

Conditional on their state vector, an individual receives a draw from the joint distribution of wages and task

complexity, which characterize the requirements of the occupation on both mental and physical dimensions.

Conditional on this offer and on their state variables, they simultaneously decide whether to enroll in school

(at), whether to work (et), and to what extent to smoke (st).11 If they decide to work, wages (wt) and

occupational requirements, both mental (mt) and physical (pht), are observed. At the end of the period,

the individual decides whether or not to leave the sample through attrition (Lt) as a function of their state

variables and their age t behaviors. As age transitions to t+ 1, the state vector Θt+1 is updated given age t

behavior and outcomes conditional on remaining in the sample.

10For ease of notation, we omit the individual i subscript where possible.11Working does not preclude school attendance.

11

4.1 Structural Equations 4 DYNAMIC MODEL

t− 1

Θt atetst

Period t

mt

phtwt

Lt

Θt+1

t+ 1

Implicit in the timing of the model is an important identification assumption. Theory suggests that

employment, education, and smoking should be chosen simultaneously as a function of the same variables.

Furthermore, because wages and tasks are only observed conditional on working, outcomes for wages and

tasks are also a function of the same state variables and exogenous characteristics as the behaviors. Put

differently, because the wage/task offer is made as a function of the state vector and exogenous character-

istics, behavior and outcomes are jointly determined. We discuss further identification assumptions (i.e.,

exclusion restrictions) below. Rather than estimate the fully structural, forward-looking model, we estimate

an approximation, which captures the dynamics of the system. Specifically, solution to the structural model

would yield demand equations for smoking, schooling, and employment:

p(st = s) = s(Θt, Xt, Pt, µs, εst ) (1)

p(at = a) = a(Θt, Xt, Pt, µa, εat ) (2)

p(et = e) = e(Θt, Xt, Pt, µe, εet ) (3)

where, s takes values 0, 1, and 2, corresponding to none, light, and heavy smoking, respectively, and

Equation 2 captures the binary decision to attend school. Equation 3 is our model of employment, where e

takes values of 0, 1, and 2, representing none, part-time, and full-time work. The state vector is denoted Θt,

and Xt represents exogenous individual characteristics. Pt is a price vector that captures cigarette prices

and tobacco control policies, as well as local labor market conditions that may shift the decisions to seeking

schooling and/or work. The µ and ε terms represent the error structure of each equation, which we define

below.

12

4.2 Unobserved Heterogeneity, Initial Conditions, and Identification 4 DYNAMIC MODEL

Conditional on working, Equations 4 and 5 describe observations of mental and physical task complexity,

mt = m(Θt, Xt, Pet , µ

m, εmt |et > 0) (4)

pht = ph(Θt, Xt, Pet , µ

ph, εpht |et > 0) (5)

both of which are continuous, linear equations, where mt and pht are defined between 0 and 1. Also

conditional on working, Equation 6 describes the (log) wage conditional on the state vector:

wt = w(Θt, Xt, Pet , µ

w, εwt |et > 0) (6)

Aside the error structure, the only difference between behavioral Equations 1-3 and outcome Equations 4-6

is the the price vector P et , which represents only a subset of the variables in Pt. We discuss these exclusions

below. Finally, Equation 7 describes the probability of sample attrition:

p(Lt = l) = l(st, et, at,mt, pht, wt,Θt, Xt, µL, εLt ) (7)

Equations 1-7 represent the per-period system of dynamic equations that we estimate via full-information

maximum likelihood. In what follows, we discuss the error structure of the model (i.e., µ and ε), the initial

conditions, exclusion restrictions, and the form of the likelihood function.

4.2 Unobserved Heterogeneity, Initial Conditions, and Identification

We handle unobserved heterogeneity (e.g., unobserved variables that shift both the probability of smoking

and the realization of wages) by decomposing the error structure of each equation into a permanent compo-

nent (i.e., µ) and an i.i.d. component (i.e., ε). We take a semi-parametric approach in the sense that while

the ε term is assumed to follow an extreme value type 1 distribution for all discrete outcomes and a normal

distribution for all continuous outcomes, we make no distributional assumption on the joint distribution

of permanent unobserved heterogeneity µ. Instead, we estimate a step-function of discrete support points

that are allowed to vary by equation. Intuitively, our discrete approach implies a set of unobserved “types”

13

4.2 Unobserved Heterogeneity, Initial Conditions, and Identification 4 DYNAMIC MODEL

which jointly determine all outcomes and behaviors of the model.12 We allow each instance of the perma-

nent disturbance µ. to take one of four points: µ ∈ {µ.1, . . . , µ.4}, and we estimate the µ parameters along

with the probability that an individual’s behavior and outcomes are consistent with each of the four types

jointly with other parameters in the model.13 For example, if a group of individuals have a particularly low

discount rate, which is unobserved in our data, we would expect to estimate one type where the value of

µ in the smoking equation was negative and the value of µ in the school enrollment equation was positive.

We select four points of support because a fifth does not improve the likelihood function value and in some

cases the estimation algorithm fails to differentiate types within some equations when considering a fifth

point.

Equations 1-7 are dynamic in the sense that each dependent variable depends on past behaviors and

outcomes; however, at age 16, our data do not include information on these lagged variables. We solve this

initial condition problem in two ways. First, we construct our data such that all individuals are enrolled

in school at age 16, thus eliminating variation in the school enrollment initial condition. Second, following

Keane & Wolpin (1997), we allow initial conditions for light (heavy) smoking and part-time (full-time) labor

supply to affect the permanent unobserved component µ. Specifically, we let the probability that individual

i exhibits behavior consistent with type k be given as:

τk = ln(P (µi = µk)

P (µi = µ1)) = ψk0 + ψk11[si,t=16 = 1] + ψk21[si,t=16 = 2] + ψk31[ei,t=16 = 1] + ψk41[ei,t=16 = 2] (8)

Thus, age 16 smoking and labor market behavior affect the probability of being each type. If, for example,

type 2 individuals are more likely to smoke cigarettes between ages 17 and 30, then we would expect ψ2

to be positive because individuals observed to be smoking heavily at age 16 should be more likely to be

observed smoking heavily at older ages.

At this point, the model is technically identified. By allowing the initial conditions to affect future

behavior and outcomes through µ, and by assuming that some equations exhibit non-linear functional

12See Heckman & Singer (1984); Mroz (1999); Darden et al. (2018) for further details. This approach is designed tocapture between-individual permanent unobserved heterogeneity while still allowing for the estimation of coefficients ontime-invariant individual specific variables.

13Estimation of these points is possible up to the normalization that the first point of support µ.1 = 0 for all equations.

14

4.2 Unobserved Heterogeneity, Initial Conditions, and Identification 4 DYNAMIC MODEL

forms through assumptions place on ε, the likelihood function is maximized by a unique set of parameters.

To estimate the system, we maximize the log-likelihood function with respect to the parameters that dictate

the distribution of unobserved heterogeneity (i.e., µ2 . . . µ4 for each equation plus the ψ parameters, which

dictate the share of individuals consistent with each type) and per-period behavior and outcomes. The

latent factor µ is integrated out of the likelihood function by constructing the µ-type specific contribution

to the likelihood function and then weighting each type specific likelihood value by the probability that a

given person’s behavior is consistent with that type. The likelihood function value for period i is given as:

Li(Θ) =4∑

k=1

τk

{T (i)∏t=2

{ 2∏s=0

P (st = s|µk)1[st=s]1∏

a=0

P (at = a|µk)1[at=a]1∏e=0

P (et = e|µk)1[et=e] ×

×φ(wt − (wt|et = 1, µk))φ(mt − (mt|et = 1, µk))φ(pht − ( ˆpht|et = 1, µk))×

×1∏l=0

P (lt = l|µk)1[lt=l]}}

(9)

(10)

We estimate the model separately for men and women, and we evaluate the estimated models via simula-

tion.14

While uniqueness of the parameters is guaranteed by our functional form assumptions, our identification

argument is also informed by economic theory via exclusion restrictions. As noted above, the timing of the

model implies that schooling, employment, and smoking decisions are made simultaneously as a function

of the same variables. Included in Equations 1-3 is a vector Pt, which includes local area cigarette prices,

excise taxes, and the presence of local indoor smoking bans in restaurants, bars, and workplaces. Pt

also includes local area labor demand variables such as the manufacturing share of employment, and the

unemployment rate. Importantly, variables that dictate local labor market conditions are included in P et ,

which shift equations for mental reasoning, physical strenuousness, and log wages, but smoking policy and

price variables in Pt are excluded from P et . Specifically, we decompose the price vector Pt = {P st , P et }14The models are slightly different for women and men because we control for lagged pregnancy in the female model.

Clearly pregnancy is endogenous in that fertility may be chosen jointly with smoking and employment decisions. We haveestimated a model in which we treat fertility as endogenous and it does not change our results. These results are availableupon request.

15

4.2 Unobserved Heterogeneity, Initial Conditions, and Identification 4 DYNAMIC MODEL

such that the vector of tobacco prices P st is excluded from the outcome equations. The assumption is that

cigarette prices, taxes, and regulations (e.g., indoor smoking bans) only affect wages through their effect

on contemporaneous smoking behavior. Furthermore, the entire vector Pt−1 is excluded from period t

behavior and outcomes, which assumes that lagged prices, policies, and labor market conditions only affect

period t behavior and outcomes through lagged behavior and outcomes. As with any exclusion restriction,

the assumptions laid out here are untestable; however, elements of P st are jointly predictive of smoking

behavior.15

Finally, in addition to theoretically motivated exclusion restrictions, the dynamics of the system along

with our treatment of unobserved heterogeneity handle a variety of competing explanations for patterns in

the data. For example, similar to Farrell & Fuchs (1982), we find a significantly negative correlation between

smoking behavior and years of schooling. One possibility is that there is something transformative about

education that causes individuals to choose not to smoke. If so, our model captures this explanation because

current period smoking behavior is a function of both the lagged school enrollment decision as well as the

cumulative years of schooling up to the current period. Alternatively, the negative correlation between

smoking and schooling may be the result of unobserved “third” variable(s) that drive both behaviors.

Indeed, Farrell & Fuchs (1982) demonstrate that nearly all of the negative correlation between years of

schooling and smoking at age 24 is explained by smoking behavior at age 17, and those authors argue that

unobserved variables (e.g., rates of time preference) drive both smoking and schooling choices. Our model

also allows for this possibility because we allow the permanent unobserved component (i.e., µa) of the school

enrollment equation to be correlated with the permanent unobserved component (i.e., µs) of the smoking

equation.

15The F-statistic on the test that all coefficients on cigarette prices, taxes, and regulations are zero in the smoking equationis 55.47 (p-value=0.000) for men and 43.45 (p-value=0.0173) for women.

16

5 RESULTS

5 Results

5.1 Parameter Estimates

Table 5 presents parameter estimates from the wage equation. Columns labeled as “separately” present

coefficients and standard errors from estimation of the wage equation separately from other equations in the

model, and columns labeled “jointly” present estimates from our preferred model in which all equations are

estimated together with correlated errors. Hence, columns labeled jointly present estimates of µw2 . . . µw4 .

In our preferred system model, the parameter estimates themselves are difficult to interpret because of

the complex dynamics. For example, smoking affects wages both directly, through direct effects of lagged

smoking and its interaction with exogenous age and gender, but also through the stock of years of smoking

and smoking cessation. Furthermore, interactions between lagged smoking and a quadratic of age allow for

a flexible age profile by smoking status, but they make inferring marginal effects difficult. Thus, we wait to

interpret the role of smoking until the simulations of the model are discussed.16 All results that follow are

from estimation of the joint dynamic system.

Table 5 Goes Here

While interpretation of individual parameters within the system is difficult, the estimated parameters of

the unobserved heterogeneity distribution demonstrate the importance of allowing for correlation in the

errors across equations. Table 6 presents these parameters along with estimates of the ψ parameters, which

relate initial conditions to unobserved type. For example, for women, light smoking at age 16 implies that

type 3 behavioral and outcomes patterns are most likely (ψ31 = 0.561). Relative to type 1 individuals, from

ages 17-30, type 3 individuals are statistically more likely to smoke heavily, less likely to be enrolled in

school, less likely to be working part-time or full-time, and yet they earn higher wages while working in less

mentally rigorous and more physically strenuous occupations.

Table 6 Goes Here

16Parameter estimates from other equations are presented in the Appendix.

17

5.2 Simulations 5 RESULTS

Rather than rely on interpretation of the parameter estimates, we simulate the estimated model, using the

exogenous characteristics of our sample while updating the endogenous variables. Formally, we expand the

longitudinal records of the 2,675 women and 2,854 men by 50, and we endow each person with a complete set

of error draws, including a draw from the unobserved “type” distribution based on their age 16 smoking and

labor supply behavior. We simulate behavior and outcomes forward for each gender separately, taking care

to update the state vector based on the simulated behaviors and outcomes. For example, if an individual is

simulated to smoke in year t, we update the years of smoking state variable in t+ 1 to reflect this behavior,

regardless of if the person in question actually smoked in t. In the event that a sample individual leaves

the sample through attrition but we do not simulate that person to leave, we assume that the exogenous

characteristics stay fixed to the last actual observation.

Figure 3 Goes Here

Figure 3 demonstrates that our estimated models capture the age profiles of smoking and wages for both men

and women. Here, we compare the simulated behaviors and outcomes among those simulated to remain in

the sample and the observed behaviors and outcomes of those who have yet to leave through attrition. These

simulations represent the baseline model. All behaviors and outcomes are determined by gender-specific

estimated models, and they demonstrate that the combination of individual exogenous characteristics plus

the estimated models can generate the same patterns of endogenous variables that we observed in our data.

5.2 Simulations

5.2.1 Smokers vs. Never Smokers

To evaluate the smoking wage gap, we impose different smoking patterns and simulate the corresponding

behaviors and outcomes. That is, we conduct our simulations just as those in Figure 3, but rather than allow

smoking behavior to evolve according to the model, we impose different smoking patterns. As a natural

starting point, we simulate our model imposing smoking behavior at every age and in every equation.17 This

17We do not impose smoking behavior on the initial conditions in any simulation, as such a change would shift the estimateddistribution of unobserved heterogeneity. Instead, age 16 smoking behavior stays fixed, and it informs the probabilities ofeach type for each individual.

18

5.2 Simulations 5 RESULTS

simulation captures the total marginal effect of smoking on wages in the sense that smoking affects wages

both directly in the wage equation and indirectly through its effects on school enrollment and employment

decisions and occupation outcomes. Figures 4a and 4b presents the resulting differences in simulated wages

for those assumed to always smoke lightly (heavily) relative to those simulated to never smoke. In stark

contrast to the descriptive evidence presented in Figure 1, the smoking wage gap for women (men) at

age 30 is only 14.8% (9.3%) for heavy smoking. The considerable attenuation of these results reflects the

importance of unobserved heterogeneity in the joint determination of smoking and labor market outcomes.

Furthermore, the contrast between results in Figures 1 and 4(a and b) is only evident for heavy smoking -

the results are consistent for light smoking.

Figure 4 Goes Here

5.2.2 Smoking Cessation

Figures 4c and 4d explore how smoking cessation affects wages. Specifically, the figures present simulation

results relative to a simulation in which no one smokes for heavy smokers who continue smoking (identical

to those in Figure 4a and 4b) and heavy smokers who quit at age 24. For women, the effect of cessation

is immediate: wages increase significantly in the year of cessation, which suggests that the smoking wage

gap for women may depend on spot mechanisms when wages are determined. For men, wages increase only

gradually, suggesting that life-cycle effects are more important.

5.2.3 Contemporaneous Impact of Smoking

Next, following Gilleskie et al. (2017), we isolate contemporaneous mechanisms that drive wage differences

from mechanisms that originate prior to wage outcomes being determined. This is done by comparing

simulated wages in an environment where no one ever smokes to simulated wages assuming everyone smokes

only in the wage equation. In other words, the dynamic contribution of smoking in the determination of

schooling, employment, occupation, and attrition is shut down. Thus, smoking is only allowed to enter the

model contemporaneously in the determination of wages. Figure 5a and 5b present the differences in log

19

5.2 Simulations 5 RESULTS

wages between these simulations for women and men, respectively. For women, the majority of the total

smoking wage gap (i.e., 14.8%) remains at age 30 - wages are roughly 10% lower for heavy smokers relative

to nonsmokers. This result implies that contemporaneous mechanisms - those mechanisms that occur at the

time wages are determined - explain 67% of the smoking wage gap for women. In contrast, contemporaneous

mechanisms explain only 11% of the male gap. By age 30, the penalty for heavy smoking among men is

only about 1% when only considering contemporaneous mechanisms. These results are consistent with

the smoking cessation results in Figures 4c and 4d, which demonstrate that upon quitting, female wages

increase much faster than do male wages.

Figure 5 Goes Here

5.2.4 Smoking Wage Gap and Productivity

Finally, we explore heterogeneity in both the total marginal effect and the contemporaneous effect by the

degree of task intensity within occupations. If, for example, the contemporaneous effect of smoking on

wages increases (i.e., the wage gap increases) with the degree of physical strenuousness on the job, then

differences in productivity conditional on human capital may explain the smoking wage gap. Alternatively,

if only the total effect of smoking on wages increases in the degree of physical strenuousness, then smoking

may affect the growth in occupational task complexity, which drives current wage differences.

Figure 6 Goes Here

Figure 6 presents estimates of the smoking wage gap at age 30 at each of 20 bins of the distributions of

mental reasoning and physical strenuousness. In each figure, we present both the total effect (dashed line)

of smoking and the contemporaneous effect (solid line). For women, the smoking wage gap increases as

occupations become more mentally taxing (panel a), but the gap is constant across different levels of physical

strenuousness (panel c). For men, similar to women, the total wage gap grows strongly in the degree of

mental reasoning. Interestingly, however, the total effect of smoking on wages actually shrinks for men

20

6 DISCUSSION AND CONCLUSION

in the degree of physical strenuousness. Both the flat wage gap for women and decreasing wage gap for

men as physical strenuous increases is inconsistent with a productivity explanation for the smoking wage

gap. Results in Figure 6 also shed light on the differential explanatory power of life-cycle mechanisms by

gender. For men, the decreasing smoking wage gap in physical strenuousness creates an incentive for male

smokers to select into physically strenuous occupations. Because physically strenuous occupations require

less formal schooling, results in Figure 6d also create a disincentive for male smokers to enroll in costly

schooling.

6 Discussion and Conclusion

This research employs a dynamic model of human capital accumulation, wage determination, occupational

outcomes, and smoking behavior that allows us to decompose the contemporaneous and life-cycle effects

of the smoking wage gap. The model is dynamic in the sense that current period outcomes of wages and

occupational task requirements are a function of both current period and past work, smoking, and education

decisions. We allow errors that dictate these behavioral and outcome equations to be flexibly correlated,

which helps to adjust for permanent unobserved heterogeneity in their joint determination.

We find that the total selectivity corrected penalty for heavy smoking is 14.8% for women and 9.3% for

men at age 30. For women, two-thirds of this gap is due to contemporaneous effects, perhaps due to employer

preferences, discrimination, or lower productivity; although productivity effects appear to be concentrated

in jobs with higher mental reasoning. For men, the contemporaneous difference, about a 1% pay gap, is only

11% of the total penalty associated with smoking. This differential in the mechanisms behind the smoking

gap has implications for the benefits of smoking cessation. For women, smoking cessation at age 24 results

in an immediate increase in wages. However, for men the attenuation of the penalty is much smaller as

the differential investment in human capital and occupational outcomes drive the smoking penalty. Our

differential results by gender are consistent with differences the obesity literature, which has found larger

spot penalties associated with being obese in women’s wages relative to men’s (Cawley, 2013; Mason, 2012;

Gilleskie et al., 2017).

These differential mechanisms by gender also highlight the importance of dynamics and institutional

21

6 DISCUSSION AND CONCLUSION

characteristics when studying disparities. The spot wage differences by smoking, which is itself a choice, are

not just a function of one’s current employer’s preferences, but, rather, they stem from the lived experience

over many years. Smokers make different choices at different points in their labor market and educational

experiences and these life-cycle effects compound over time, especially for men.

The smaller life-cycle effect for women has implications for the overall gender gap. In general, the

literature has found that women have a greater return to education than men, with some evidence that

education appears to dampen the negative effect of discrimination, tastes, and circumstances (DTC) on

wages for women (Dougherty, 2005). Given that the negative impact of smoking on school enrollment is

relatively small for women, the results suggest that smoking removes at least some of the protection from

DTC on wages that is provided to women from education.

Life-cycle effects for both men and women have implications for smoking policies. It appears that the

decision to smoke is negatively correlated with the decision to obtain education and to enter lower paying

occupations for both men, and to a lesser degree, women. This suggests that the return to youth smoking

prevention programs will have much larger returns than have been shown in the literature. For example, the

impact of smoking on lifetime earnings, even with cessation at a relatively early age, suggests that the return

to youth prevention programs such as “The Real Cost” are under-counted if they do not take into account

the lost tax revenue and productivity associated with smoking over the lifetime of the youth (MacMonegle

et al., 2018).

22

REFERENCES REFERENCES

References

Auld, Christopher. 2005. Smoking, Drinking, and Income. Journal of Human Resources, 40(2), 505–518.

Autor, David H. 2019. Work of the Past, Work of the Future. American Economic Association: Papers and

Proceeding, 109(5), 1–32.

Bartik, Timothy, Biddle, Stephen, Hershbein, Brad, & Sotherland, Nathan. 2017. WholeData:

Unsuppressed County Business Patterns Data: Version 1.0. Tech. rept. W. E. Upjohn Institute for

Employment Research.

Bauer, J., Hyland, A., Li, Qiang, Steger, C., & Cummings, M. 2005. A Longitudinal Assessment of the

Impact of Smoke-Free Worksite Policies on Tobacco Use. American Journal of Public Health, 95(6),

1024–1029.

Berman, M, Crane, R, Seiber, E, & Munur, M. 2014. Estimating the cost of a smoking employee. Tobacco

Control, 23(5).

Callison, Kevin, & Kaestner, Robert. 2014. Do Higher Tobacco Taxes Reduce Adult Smoking? New

Evidence of the Effect of Recnt Cigarette Tax Increases on Adult Smoking. Economic Inquiry, 52(1),

155–172.

Carpenter, C., & Cook, P. 2008. Cigarette taxes and youth smoking: New evidence from national, state,

and local Youth Risk Behavior Surveys. Journal of Health Economics, 27(2), 287–299.

Cawley, J. 2013. The economics of obesity. NBER Reporter, 7–10.

Chaloupka, Frank, Yurekli, A., & Fong, G. 2012. Tobacco taxes as a tobacco control strategy. Tobacco

Control, 21, 172–180.

Cotti, Chad, Courtemanche, Charles, Maclean, Catherine, Nesson, Erik, Pesko, Michael, & Tefft, Nathan.

2020. THE EFFECTS OF E-CIGARETTE TAXES ON E-CIGARETTE PRICES AND TOBACCO

PRODUCT SALES: EVIDENCE FROM RETAIL PANEL DATA. NBER Working Paper.

23

REFERENCES REFERENCES

Creamer, MR, Wang, TW, Babb, S, & et al. 2018. Tobacco Product Use and Cessation Indicators Among

Adults — United States. MMWR Morb Mortal Wkly Rep. MMWR Morb Mortal Wkly Rep.

Darden, Michael, Gilleskie, Donna, & Strumpf, Koleman. 2018. Smoking and Mortality: New Evidence

from a Long Panel. International Economic Review, 59(3), 1571–1619.

Davis, B., & Carpenter, C. 2009. Proximity of fast-food restaurants to schools and adolescent obesity.

Journal Information, 99(3).

Doll, R., Peto, R., Boreham, J., Gray, R., & Sutherland, I. 2004. Mortality in Relation to Smoking: 50

Years’ Observations on Male British Doctors. British Medical Journal, 328, 1519–1528.

Dougherty, Christopher. 2005. Why Are the Returns to Schooling Higher for Women than for Men? Journal

of Human Resources, 40(4), 969–988.

Farrell, Phillip, & Fuchs, Victor R. 1982. Schooling and Health: The Cigarette Connection. Journal of

Health Economics, 1(3), 217–230.

Fichtenberg, C., & Glantz, S. 2002. Effect of smoke-free workplaces on smoking behaviour: systematic

review. BMJ, 325(7357), 188.

Gilleskie, Donna, Han, Euna, & Norton, Edward. 2017. Disentangling the contemporaneous and dynamic

effects of human and health capital on wages over the life cycle. Review of Economic Dynamics, 25,

350–383.

Grafova, Irina, & Stafford, Frank. 2009. The Wage Effects of Personal Smoking History. Industrial Labor

Relations Review, 62(3), 381.

Hansen, B., Sabia, J., & Rees, D. 2017. Have Cigarette Taxes Lost Their Bite? New Estimates of the

Relationship between Cigarette Taxes and Youth Smoking. American Journal of Health Economics,

3(1).

Heckman, James J., & Singer, Burton. 1984. A Method for Minimizing the Impact of Distributional

Assumptions in Econometric Models for Duration Data. Econometrica, 52(2), 271–320.

24

REFERENCES REFERENCES

HHS. 1964. Smoking and Health: Report of the Advisory Committee to the Surgeon General of the Public

Health Service. Report. US Department of Health, Education, and Welfare.

Hotchkiss, Julie L., & Pitts, M. Melinda. July 2013. Even One Is Too Much: The Economic Consequences

of Being a Smoker. Working Paper 2013-3. Federal Reserve Bank of Atlanta.

Keane, Michael, & Wolpin, Kenneth. 1997. The Career Decisions of Young Men. Journal of Political

Economy, 105(3), 473–522.

Lemieux, P. 2019. CONSUMER SURPLUS IN THE FDA’S TOBACCO REGULATIONS WITH

APPLICATIONS TO NICOTINE REDUCTION AND E-CIGARETTE FLAVORS. Report. Reason

Foundation.

MacMonegle, A.J., Nonnemaker, J., Duke, J.C., Farrelly, M.C., X., Zhao, Delahanty, J.C., Smith, A.A.,

Rao, P., & J.A., Allen. 2018. Cost-Effectiveness Analysis of the Real Cost Campaign’s Effect on Smoking

Prevention. American Journal of Preventative Medicine, 55(3), 319–325.

Mason, Katherine. 2012. The Unequal Weight of Discrimination: Gender, Body Size, and Income Inequality.

Journal of Human Resources, 59(3), 411–435.

Mroz, Thomas. 1999. Discrete Factor Approximations in Simultaneous Equation Models: Estimating the

Impact of a Dummy Endogenous Variable on a Continuous Outcome. Journal of Econometrics, 92(2),

233–274.

Siegel, M., Albers, A., Cheng, D., W., Hamilton, & Biner, L. 2009. Local Restaurant Smoking Regulations

and the Adolescent Smoking Initiation Process. Archives of Pediatric Adolescent Medicine, 162(5),

477–483.

Sloan, Frank, Ostermann, Jan, Conover, Christopher, Taylor, Donald, & Picone, Gabriel. 2006. The Price

of Smoking. MIT press.

van Ours, Jan. 2004. A pint a day raises a man’s pay; but smoking blows that gain away. Journal of Health

Economics, 23, 863–886.

25

REFERENCES REFERENCES

Xu, Xin, Bishop, Ellen, Kennedy, Sara, Simpson, Sean, & Pechacek, Terry. 2015. Annual Healthcare

Spending Attributable to Cigarette Smoking: An Update. American Jouranal of Preventive Medicine,

48(3), 326–333.

26

A MAIN TABLES

A Main Tables

Table 1: Sample Construction

# Ind. Inclusion Criteria8,984 Base Sample of NLSY 1997.8,804 Complete school enrollment information8,181 16 or younger in 19977,590 Complete smoking information6,919 Complete employment information6,755 Never in the military6,520 Complete location (county and state) information5,529 Enrolled in School and not pregnant at age 16

Notes: The 5,529 individuals we study generate 93,993 per-son/year observations. The sample includes 2,675 women and2,854 men.

27

A MAIN TABLES

Table 2: Exogenous and Pre-Determined Variables by Smoking

Women, n=45,475 Men, n=48,518Non-Smokers Light Heavy Non-Smokers Light Heavy

0.738 0.086 0.176 0.663 0.112 0.225Exogenous Characteristics

Age 23.523 22.508 23.269 23.437 22.850 23.337Asvab 41.935 45.968 41.298 41.104 39.332 37.323Black 0.284 0.168 0.149 0.247 0.204 0.209Hispanic 0.233 0.233 0.134 0.224 0.288 0.120MSA Unemployment Rate *10 64.386 60.376 62.085 62.574 61.337 61.350Population 0.381 0.400 0.365 0.380 0.378 0.377Manufacturing Share 0.120 0.121 0.137 0.120 0.120 0.134Service Share 0.511 0.511 0.492 0.510 0.505 0.500Computing Share 0.059 0.060 0.061 0.059 0.060 0.059Earnings per Employee 37.378 37.336 34.842 37.303 36.158 35.605Share Missing 0.037 0.029 0.034 0.036 0.044 0.035Father’s Education Low 0.176 0.125 0.160 0.173 0.190 0.183Father’s Education Med. 0.313 0.296 0.358 0.304 0.280 0.366Father’s Education High 0.335 0.426 0.284 0.348 0.342 0.295Mother’s Education Low 0.219 0.142 0.174 0.184 0.214 0.165Mother’s Education Med. 0.321 0.308 0.386 0.349 0.323 0.387Mother’s Education High 0.409 0.509 0.388 0.398 0.376 0.384Mother Education Missing 0.051 0.041 0.053 0.069 0.087 0.064Father Education Missing 0.176 0.153 0.198 0.175 0.189 0.157Pack Sales Per Capita (within State) 60.568 61.928 64.437 59.498 59.989 64.165Gross Cigarette Tax Rev. (m $) 60.325 62.110 54.445 61.861 59.132 54.777State Cigarette Tax 1.047 1.045 1.081 1.096 1.040 1.065Local Area Cigarette Tax 0.063 0.055 0.052 0.065 0.050 0.044Mean Cost per Pack 5.228 5.153 5.252 5.298 5.170 5.223Workplace Indoor Smoking BansComplete Ban, Partial County 0.308 0.330 0.223 0.327 0.319 0.213Partial Ban, Partial County 0.629 0.652 0.573 0.632 0.645 0.548Comlete Ban, Complete County 0.008 0.004 0.007 0.011 0.014 0.018Partial Ban, Complete County 0.032 0.025 0.033 0.028 0.031 0.032Statewide Complete Ban 0.233 0.179 0.210 0.212 0.188 0.235Statewide Partial Ban 0.754 0.796 0.770 0.781 0.767 0.735Missing Ban Information 0.003 0.003 0.003 0.003 0.004 0.010

Pre-Determined CharacteristicsJob Experience (Years) 4.991 4.326 4.662 4.859 4.315 4.602Job Tenure (Years) 0.991 0.734 0.710 1.062 0.855 0.790Duration Smoking (Years) 1.492 4.635 7.650 1.780 4.906 7.388Cessation Smoking (Years) 1.792 0.764 0.197 1.769 0.700 0.189Years of Schooling 13.402 12.987 12.119 13.006 12.677 11.895Years≥12 0.702 0.662 0.482 0.640 0.611 0.464Years≥16 0.213 0.152 0.085 0.172 0.127 0.063

Notes: Table presents exogenous and pre-determined variables by smoking status. Light smoking isdefined as smoking on between 1 and 20 of the previous 30 days from each NLSY wave. Heavy smokingis defined as smoking on 20 or more of the previous 30 days from each NLSY wave. Data are from ourmain estimation sample, where the person/age sample sizes for women and men are 45,475 and 48,518,respectively. All dollar figures are in 2015 dollars.

28

A MAIN TABLES

Table 3: Factor Construction from O*NET

Mental Reasoning Factor Physical Strenuousness FactorMeasurement Weight Measurement WeightOral Expression 0.357 Spend Time Climbing 0.398Written Expression 0.357 Outdoors Under Cover 0.346Writing 0.349 Outdoors No Cover 0.336Oral Comprehension 0.309 Exposed Whole Body 0.301Complex Problem Solving 0.309 Exposed Extreme Light 0.265Written Comprehension 0.296 Spend Time Balancing 0.263Reading Comprehension 0.291 Exposed Extreme Temp 0.251Fluency of Ideas 0.237 Confined Space Work 0.248Deductive Reasoning 0.233 Spend Time Kneel Crouch 0.234Inductive Reasoning 0.228 Indoors No AC 0.198Science 0.173 Dynamic Flexibility 0.188Memorization 0.152 Gross Body Equilibrium 0.165Analytical Thinking 0.121 Exposed To Hazardous 0.137Category Flexibility 0.114 Exposed Loud Noises 0.109Mathematical Reasoning 0.046 Exposed To Nicks 0.089Critical Thinking 0.043 TimeBending Twisting Body 0.078Speed of Closure 0.011 Extent Flexibility 0.059Number Facility 0.010 Speed Of Limb Movement 0.048Mathematics -0.055 Gross Body Coordination 0.028Flexibility of Closure -0.105 Spend Time Walk Run 0.023

Spend Time Sitting 0.022Stamina 0.010Dynamic Strength 0.008Spend Time Repetitive Motions -0.001Multi-limb Coordination -0.008Trunk Strength -0.015Response Orientation -0.020Static Strength -0.024Reaction Time -0.025Rate Control -0.025Explosive Strength -0.031Spend Time Standing -0.044Manual Dexterity -0.073Indoors AC -0.187

Notes: Table demonstrates the 2010 occupation code O*NET measurementsand the associated weights from the first principal component of principal com-ponent analysis. The first principal component of the first column measurementsexplains 53.08% of the variation, whereas the first principal component of thethird column explains 63.22 % of the variation.

29

A MAIN TABLES

Table 4: Endogenous Variables by Smoking

Women, n=45,475 Men, n=48,518Non-Smokers Light Heavy Non-Smokers Light Heavy

0.738 0.086 0.176 0.663 0.112 0.225School Enrollment 0.346 0.395 0.230 0.312 0.301 0.167Part-Time Work 0.220 0.250 0.192 0.153 0.143 0.115Full-Time Work 0.513 0.486 0.510 0.586 0.563 0.593Log Wage | Working 7.176 7.117 6.969 7.295 7.231 7.165Mental Reasoning | Working 0.459 0.435 0.394 0.417 0.397 0.358Physical Strenuousness | Working 0.209 0.217 0.238 0.360 0.373 0.433

Notes: Table presents endogenous variables by smoking status. Mental reasoning and physicalstrenuousness are occupation-specific measures of task requirements constructed from ONET data(see Table 3). Light smoking is defined as smoking on between 1 and 20 of the previous 30 daysfrom the NLSY 1997 interview. Heavy smoking is defined as smoking on 20 or more of the previous30 days from the NLSY 1997 interview. Data are from our main estimation sample, where theperson/age sample sizes for women and men are 45,475 and 48,518, respectively.

30

A MAIN TABLES

Table 5: Log Wage Equation Parameters

Women MenSeparately Jointly Separately Jointly

Coef. St. Err. Coef. St. Err. Coef. St. Err. Coef. St. Err.L. Light Smoking -0.021 0.031 -0.022 0.033 -0.003 0.032 0.032 0.034

L. Heavy Smoking -0.032 0.027 -0.012 0.027 0.023 0.026 0.041 0.026Age*L. Light Smoking 0.005 0.009 0.001 0.010 -0.008 0.009 -0.016 0.009

Age Squared/100 * L. Light Smoking -0.037 0.057 0.008 0.062 0.069 0.054 0.112 0.055Age*L. Heavy Smoking -0.014 0.007 -0.010 0.007 -0.019 0.007 -0.011 0.007

Age Squared/100 * L. Heavy Smoking 0.051 0.044 0.027 0.042 0.097 0.042 0.053 0.040L. Pregnancy 0.003 0.009 0.011 0.009 0.000 . 0.000 .

L. School Enrollment -0.073 0.008 -0.081 0.008 -0.087 0.009 -0.098 0.008L. Mental Reasoning 0.655 0.019 0.329 0.016 0.599 0.019 0.221 0.015

L. Physical Strenuousness -0.266 0.023 -0.108 0.022 0.194 0.014 0.120 0.013L. Child in the Home -0.026 0.008 -0.046 0.007 0.087 0.009 0.067 0.006

L. Part-Time Work -0.081 0.010 -0.040 0.009 -0.115 0.011 -0.048 0.010L. Full-Time Work 0.024 0.010 0.028 0.010 0.007 0.010 0.021 0.010

L. Mental Reasoning Missing 0.285 0.031 0.159 0.024 0.353 0.028 0.194 0.018L. Years of Schooling≥12 -0.057 0.010 -0.075 0.009 -0.030 0.010 -0.022 0.007L. Years of Schooling≥16 0.042 0.013 0.060 0.011 -0.014 0.015 0.020 0.010

L. Years of Schooling 0.034 0.003 0.047 0.003 0.029 0.003 0.037 0.002Job Tenure Years 0.018 0.002 0.009 0.002 0.020 0.002 0.010 0.002

Job Experience Years 0.016 0.002 0.019 0.001 0.010 0.002 0.016 0.001Smoking Duration 0.003 0.001 0.002 0.001 -0.001 0.001 -0.001 0.001Smoking Cessation 0.000 0.001 0.001 0.001 0.004 0.001 0.003 0.001

Age 0.003 0.004 0.009 0.004 0.014 0.004 0.021 0.004Age Squared/100 -0.046 0.022 -0.063 0.020 -0.078 0.023 -0.100 0.020

Black -0.013 0.013 0.008 0.012 -0.089 0.013 -0.139 0.011Hispanic 0.012 0.013 0.036 0.013 -0.010 0.012 -0.036 0.010

Asvab Score 0.001 0.000 0.002 0.000 0.001 0.000 0.000 0.000Asvab Score Missing 0.039 0.010 0.038 0.010 0.000 0.010 -0.053 0.008

Survey Weight -0.003 0.005 0.016 0.005 0.007 0.006 -0.014 0.005Mother’s Education Med. -0.004 0.009 0.010 0.009 -0.006 0.009 0.004 0.008Mother’s Education High -0.011 0.009 -0.015 0.010 -0.004 0.010 0.008 0.009

Mother’s Education Missing 0.048 0.016 0.056 0.014 -0.019 0.014 0.008 0.012Father’s Education Med. -0.012 0.009 -0.015 0.008 0.008 0.009 0.002 0.008Father’s Education High 0.012 0.010 0.042 0.009 0.016 0.010 -0.002 0.008

Father’s Education Missing -0.016 0.010 0.000 0.010 -0.007 0.011 -0.030 0.009MSA UR 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Population -0.252 0.028 -0.151 0.023 -0.316 0.029 -0.233 0.024Man. Share -0.234 0.051 -0.164 0.044 -0.158 0.053 -0.172 0.042

Service Share -0.198 0.053 -0.191 0.042 -0.272 0.053 -0.285 0.040Comptuer Share 0.173 0.124 0.241 0.098 0.374 0.126 0.102 0.096

Earnings 0.011 0.000 0.010 0.000 0.010 0.000 0.009 0.000Economics Variables Missing -0.025 0.018 0.008 0.016 -0.014 0.018 -0.021 0.016

Constant 6.222 0.052 5.935 0.045 6.360 0.052 6.147 0.043µw2 0.334 0.006 0.757 0.008µw3 0.163 0.009 0.310 0.007µw4 -0.442 0.008 0.479 0.008

Notes: Estimates of the log wage equation by gender. Columns labeled “separate” present results from estimationof the log wage equation separately from other equations that dictate behavior and outcomes. Columns labeled“jointly” present results from our preferred model, which estimates all equations jointly and allows for unobservedheterogeneity. The person/age sample sizes for women and men are 45,475 and 48,518, respectively.

31

A MAIN TABLES

Table 6: Unobserved Heterogeneity Parameters

Women, n = 45, 475L. Smoking H. Smoking S. Enrollment Part-Time Full-Time

Coef. St. Err. Coef. St. Err. Coef. St. Err. Coef. St. Err. Coef. St. Err.Mass Points

µ.2 0.060 0.060 -0.248 0.073 -0.133 0.051 -0.226 0.059 0.093 0.054µ.3 -0.075 0.104 0.315 0.098 -0.347 0.081 -0.242 0.082 -0.159 0.077µ.4 0.129 0.093 0.412 0.091 0.030 0.073 0.198 0.084 -0.217 0.083

Attrition Log Wages M. Reasoning P. Strenuousnessµ.2 0.194 0.164 0.334 0.006 0.048 0.002 -0.015 0.002µ.3 -0.310 0.306 0.163 0.009 -0.027 0.003 0.095 0.003µ.4 -0.546 0.269 -0.442 0.008 -0.057 0.002 0.030 0.003

Type 2 Type 3 Type 4Age 16 Initial Conditions

Constant ψ0 -0.235 0.194 -0.040 0.304 -0.389 0.333Light Smoking ψ1 -0.272 0.222 0.561 0.260 0.444 0.248Heavy Smoking ψ2 0.072 0.127 -0.152 0.203 -0.041 0.196Part-Time Workψ3 0.232 0.307 0.339 0.442 -0.169 0.514Full-Time Workψ4 -0.519 0.093 -1.645 0.144 -1.722 0.137

Men, n = 48, 518L. Smoking H. Smoking S. Enrollment Part-Time Full-Time

Mass PointsCoef. St. Err. Coef. St. Err. Coef. St. Err. Coef. St. Err. Coef. St. Err.

µ.2 0.055 0.083 -0.628 0.088 -0.131 0.080 -0.300 0.085 0.369 0.077µ.3 0.181 0.068 -0.178 0.064 0.069 0.059 -0.099 0.068 0.354 0.059µ.4 0.044 0.076 -0.273 0.072 -0.619 0.076 -0.447 0.083 0.443 0.068

Attrition Log Wages M. Reasoning P. Strenuousnessµ.2 0.224 0.258 0.757 0.008 0.110 0.003 -0.040 0.004µ.3 0.482 0.217 0.310 0.007 0.066 0.002 -0.037 0.003µ.4 1.110 0.217 0.479 0.008 0.045 0.003 0.146 0.003

Type 2 Type 3 Type 4Age 16 Initial Conditions

Constant ψ0 -0.212 0.273 -0.090 0.219 0.118 0.232Light Smoking ψ1 -0.780 0.295 -0.348 0.209 -0.102 0.217Heavy Smoking ψ2 -0.089 0.192 0.290 0.152 0.062 0.166Part-Time Work ψ3 0.260 0.333 -0.039 0.285 0.319 0.283Full-Time Work ψ4 -0.287 0.125 0.726 0.109 -0.027 0.116

Notes: Estimates and their standard errors of the distribution of unobserved heterogeneity by gender.

32

A MAIN TABLES

Main Figures

Figure 1

a. b.

Notes: The figure presents the mean log wage conditional on working for women (a.) and men (b.) by current smokingstatus and by age. Light smoking is defined as smoking on between 1 and 20 of the previous 30 days from each NLSYwave. Heavy smoking is defined as smoking on 20 or more of the previous 30 days from each NLSY wave. Data are fromour main estimation sample, where the person/age sample sizes for women and men are 45,475 and 48,518, respectively.

33

A MAIN TABLES

Figure 2

a. b.

c. d.

e. f.

Notes: The figure presents the mean level of school enrollment and, conditional on working, the mean level of occupationalmental reasoning and physical strenuousness for women (a., c., and, e.) and men (b., d., and f.) by current smokingstatus and by age. Light smoking is defined as smoking on between 1 and 20 of the previous 30 days from each NLSYwave. Heavy smoking is defined as smoking on 20 or more of the previous 30 days from each NLSY wave. Data are fromour main estimation sample, where the person/age sample sizes for women and men are 45,475 and 48,518, respectively.

34

A MAIN TABLES

Figure 3

a. b.

c. d.

Notes: The figures represent the age path of each behavior or outcome in the data and from the baseline simulation ofour model for women (a. and c.) and men (b. and d.). Simulation results represent those simulated to remain in thesample (i.e., not attrit). In cases in which a sample individual has left the sample through attrition but we simulatethem to remain, their exogenous characteristics are fixed at their last observation and their endogenous characteristicsare updated given other simulated behaviors and outcomes. Light smoking is defined as smoking on between 1 and 20of the previous 30 days from each NLSY wave. Heavy smoking is defined as smoking on 20 or more of the previous 30days from each NLSY wave. Data are from our main estimation sample, where the person/age sample sizes for womenand men are 45,475 and 48,518, respectively.

35

A MAIN TABLES

Figure 4

a. b.

c. d.

Notes: The figure represents the simulated difference in log wages by age for different smoking behaviors relative tonon-smoking for women (a.) and men (b.). Smoking behavior is imposed on all simulated individuals from ages 17through 30. Light smoking is defined as smoking on between 1 and 20 of the previous 30 days from each NLSY wave.Heavy smoking is defined as smoking on 20 or more of the previous 30 days from each NLSY wave. Data are from ourmain estimation sample, where the person/age sample sizes for women and men are 45,475 and 48,518, respectively.

36

A MAIN TABLES

Figure 5

a. b.

Notes: Figures a. and b. represent the difference in log wages between simulations in which individuals are assumed tonot smoke in equations that affect school enrollment, employment, job task requirements, and attrition, while varioussmoking behaviors are imposed in the wage equation. Both lines are relative to the case in which a person is assumedto not smoke contemporaneously in the wage equation. Light smoking is defined as smoking on between 1 and 20 of theprevious 30 days from each NLSY wave. Heavy smoking is defined as smoking on 20 or more of the previous 30 daysfrom each NLSY wave. Data are from our main estimation sample, where the person/age sample sizes for women andmen are 45,475 and 48,518, respectively.

37

A MAIN TABLES

Figure 6

a. b.

c. d.

Notes: Each figure represents the log wage penalty, both light and heavy smoking, by the bin occupational taskbin. For each type of occupational task, we divide the simulated distribution into 20(gender-specific) bins atage 30 and calculate the wage penalty associated with always smoking heavily relative to not smoking. Heavysmoking is defined as smoking on 20 or more of the previous 30 days from each NLSY wave. Data are fromour main estimation sample, where the person/age sample sizes for women and men are 45,475 and 48,518,respectively.

38